Particle Swarm Optimization for Identification of a Flexible Manipulator System

This paper presents an investigation of system identification using parametric modeling approaches for a single-link flexible manipulator system. The utilization of a particle swarm optimization (PSO) technique for modeling of a highly non-linear system is studied in comparison to the conventional recursive least squares (RLS) technique. A simulation environment characterizing the dynamic behavior of the flexible manipulator system was first developed using finite difference (FD) approach to acquire the input-output data of the system. A bang-bang torque was applied as an input and the dynamic response of the system was investigated. A comparative assessment of the two models in characterizing the manipulator system is presented in time and frequency domains. Results demonstrate the advantages of PSO over RLS in parametric modeling. The developed model achieved will be used for control design and development in future work.